To evaluate a pattern, in particular, to evaluate a micropattern of a semiconductor, the following method has heretofore been used: A pattern to be evaluated is imaged by, for example, a scanning electron microscope (SEM) to obtain an SEM image. This SEM image is subjected to particular image processing and then a variety of dimensions are measured. In this way, a desired evaluation (inspection) result is obtained. In general, the same apparatus is used both in acquiring the image of the pattern to be evaluated and in evaluating this image.
In response to increasingly miniaturized and complicated semiconductors, there has recently been a growing need for a variety of high-level image processing. Accordingly, one method has been proposed wherein a pattern is evaluated by an apparatus different from an image acquiring apparatus. In this connection, there has been proposed a system as one example. In this system, a series of images acquired by, for example, a critical dimension (CD)-SEM is registered in an image Data Base together with acquisition conditions for these images. The images are rapidly processed by a server exclusive for image processing. In such a system, high-level image processing is rapidly performed for a great number of pattern images. To this end, a high-speed CPU is mounted on the server exclusive for image processing, and a distributed computing technique is used. This enables efficient processing. In the distributed computing, a plurality of computers connected to one another by a network perform one or more processes (hereinafter referred to as “jobs”). A distributed computing system generally comprises one computer designated as a master, and a plurality of computers called clusters. For example, if 1000 images are processed by 100 clusters, one cluster has only to process 10 images. This allows a significant reduction in processing time. Thus, if all of the clusters can be allocated to one job, image processing is performed in the shortest time.
However, provided that all the available clusters are allocated to processing when the image acquisition is simultaneous with the image processing, there may rise following problem. Specifically, the time for the image processing is shorter than the time for the image acquisition. After finishing the processing, the clusters have a waiting time (idling time) until a next image comes. As a result, the whole system may have a wasteful CPU resource.
On the other hand, if a small number of clusters are allocated in order to avoid the idling, the speed of the image processing does not catch up with the speed of the image acquisition, leading to a decrease in the throughput of the pattern evaluation.
Thus, according to the conventional art, the image acquisition time and the image processing time cannot be predicted when various kinds of pattern images have to be evaluated by various evaluation methods, for example, particularly when a semiconductor pattern is evaluated. Therefore, the number of allocated clusters cannot be optimized. This leads to disadvantages such as an increased processing time and decreased efficiency of the server.